Choice effect of linear separability testing methods on constructive neural network algorithms: An empirical study

Authors:
David A. Elizondo;J. M. Ortiz-de-Lazcano-Lobato;Ralph Birkenhead
Affiliations:
Centre for Computational Intelligence, School of Computing, Faculty of Computing Sciences and Engineering, De Montfort University, Leicester, UK;Department of Computer Science and Artificial Intelligence, University of Málaga, Campus de Teatinos, s/n Málaga 29071, Spain;Centre for Computational Intelligence, School of Computing, Faculty of Computing Sciences and Engineering, De Montfort University, Leicester, UK
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

Several algorithms exist for testing linear separability. The choice of a particular testing algorithm has effects on the performance of constructive neural network algorithms that are based on the transformation of a nonlinear separability classification problem into a linearly separable one. This paper presents an empirical study of these effects in terms of the topology size, the convergence time, and generalisation level of the neural networks. Six different methods for testing linear separability were used in this study. Four out of the six methods are exact methods and the remaining two are approximative ones. A total of nine machine learning benchmarks were used for this study.